Towards Quantum Graph Neural Networks: An Ego-Graph Learning Approach

Quantum machine learning is a fast-emerging field that aims to tackle machine learning using quantum algorithms and quantum computing. Due to the lack of physical qubits and an effective means to map real-world data from Euclidean space to Hilbert space, most of these methods focus on quantum analog...

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Bibliographic Details
Published in:arXiv.org 2024-01
Main Authors: Xing Ai, Zhang, Zhihong, Sun, Luzhe, Junchi Yan, Hancock, Edwin
Format: Article
Language:English
Subjects:
Algorithms
Decomposition
Euclidean geometry
Euclidean space
Graph neural networks
Hilbert space
Machine learning
Mapping
Mathematical models
Neural networks
Parameters
Quantum computing
Qubits (quantum computing)
Structured data
Tensors
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Summary:Quantum machine learning is a fast-emerging field that aims to tackle machine learning using quantum algorithms and quantum computing. Due to the lack of physical qubits and an effective means to map real-world data from Euclidean space to Hilbert space, most of these methods focus on quantum analogies or process simulations rather than devising concrete architectures based on qubits. In this paper, we propose a novel hybrid quantum-classical algorithm for graph-structured data, which we refer to as the Ego-graph based Quantum Graph Neural Network (egoQGNN). egoQGNN implements the GNN theoretical framework using the tensor product and unity matrix representation, which greatly reduces the number of model parameters required. When controlled by a classical computer, egoQGNN can accommodate arbitrarily sized graphs by processing ego-graphs from the input graph using a modestly-sized quantum device. The architecture is based on a novel mapping from real-world data to Hilbert space. This mapping maintains the distance relations present in the data and reduces information loss. Experimental results show that the proposed method outperforms competitive state-of-the-art models with only 1.68\% parameters compared to those models.
ISSN:2331-8422